Proposed by [1]}, VarifocalNet (VfNet) is an architecture that is designed on top of the fully convolutional one-stage object detector (FCOS) [2]}. FCOS originally proposes a pixel-wise approach similar to segmentation and utilizes a one-stage anchor free object detector. In [3]}, the idea of adaptive training sample selection (ATSS) is developed on top of the FCOS architecture.
VfNet is another improvement over this design family and has three main contributions to the baseline FCOS + ATSS approach. Firstly, VfNet proposes the so-called intersection over union-aware classification score (IACS) function, which is a scalar representation of a fusion of classification and the intersection-over-union (IoU) score. Secondly, they propose a novel loss function, namely Varifocal loss, which is designed to penalize their proposed IACS function. Varifocal loss function is based on the so-called Focal Loss [4]} that is designed to handle the imbalance problem between positive and negative samples. Verifocal loss is utilized only for negative samples; whereas, for positive samples, Binary Cross-Entropy (BCE) Loss is employed with the multiplication of the ground truth class. Finally, they propose a star-shaped bounding box feature, which they claim to be more efficient for their IACS score function. In the original paper, VfNet is tested with various backbones, computation-wise lightest one of which is the ResNet-50 [5]}, as utilized in our experiments.
